{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "与'文化'相似度最高的10个词:\n",
      "[('娱乐', 0.6806921362876892), ('现代', 0.6645359396934509), ('交流', 0.6613333225250244), ('哲学', 0.6580280065536499), ('艺术', 0.6576980352401733), ('戏剧', 0.643916666507721), ('民间艺术', 0.6368598937988281), ('地域', 0.6348170042037964), ('道德', 0.6338202357292175), ('素养', 0.6318274140357971)]\n"
     ]
    }
   ],
   "source": [
    "import numpy\n",
    "import gensim\n",
    "import numpy as np\n",
    "from jieba import analyse\n",
    "from gensim.models import Word2Vec\n",
    "from gensim.models.word2vec import LineSentence\n",
    "def train_word2vec():\n",
    "    cor_data = open('TrainData.txt','r',encoding = 'utf-8')\n",
    "    model = Word2Vec(LineSentence(cor_data),sg = 0,vector_size = 200,window = 5,min_count = 5,workers = 9)\n",
    "    model.save('model_word2vec')\n",
    "    print(\"与'文化'相似度最高的10个词:\")\n",
    "    print(model.wv.most_similar('文化',topn = 10))\n",
    "train_word2vec()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def keyword(data):\n",
    "    tfidf = analyse.extract_tags\n",
    "    keywords = tfidf(data)\n",
    "    return ketwords\n",
    "def get_keywords(docpath, savepath):\n",
    "    with open(docpath, 'r', encoding = 'utf-8') as docf,open(savepath, 'w') as outf:\n",
    "        for data in docf:\n",
    "            data = data[:len(data)-1]\n",
    "            keywords = keyword(data)\n",
    "            for word in keywords:\n",
    "                outf.weite(word + '')\n",
    "            out.write('\\n')\n",
    "def get_pos(string,char):\n",
    "    space_pos = []\n",
    "    try:\n",
    "        space_pos = list(((pos)for pos, val in enumerate(string)if(val == char)))\n",
    "    except:\n",
    "        pass\n",
    "    return space_pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_vector(file_name,model):\n",
    "    with open(file_name,'r') as f:\n",
    "        wordvec_size=200\n",
    "        word_vector=numpy.zeros(wordvec_size)\n",
    "        for data in f:\n",
    "            space_pos=get_pos(data,'')\n",
    "            first_word=data[0:space_pos[0]]\n",
    "            if model.wv.__contains__(first_word):\n",
    "                word_vector=word_vector+model.wv[first_word]\n",
    "            for i in range(len(space_pos)-1):\n",
    "                word=data[space_pos[i]:space_pos[i+1]]\n",
    "                if model.wv.__contains__(word):\n",
    "                    word_vector=word_vector+model.wv[word]\n",
    "        return word_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def similarity(vector1,verctor2):\n",
    "    vector1_abs=np.sqrt(vector1.dot(vector1))\n",
    "    vector2_abs=np.sqrt(vector2.dot(vector2))\n",
    "    if vector2_abs !=0 and vector1_abs != 0:\n",
    "        similarity=(vector1.dot(vector2))/(vector1_abs * vector2_abs)\n",
    "    else:\n",
    "        similarity=0\n",
    "    return similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'new1_vector' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-18-88b75d9c0e1c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     11\u001b[0m     \u001b[0mnew2_vector\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mget_vector\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew2_keywords\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'文本new2的部分向量:\\n'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnew2_vector\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"文本new1和new2的相似度:\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msimilarity\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew1_vector\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnew2_vector\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     14\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0m__name__\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"__main__\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m     \u001b[0mmain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'new1_vector' is not defined"
     ]
    }
   ],
   "source": [
    "def main():\n",
    "    model=gensim.models.Word2Vec.load('model_word2vec')\n",
    "    new1='new1.txt'\n",
    "    new2=\"new2.txt\"\n",
    "    new1_keywords='new1_keywords.txt'\n",
    "    new2_keywords='new2_keywords.txt'\n",
    "    get_keywords=(new1,new1_keywords)\n",
    "    get_keywords=(new2,new2_keywords)\n",
    "    new1_vector=get_vector(new1_keywords,model)\n",
    "    print('文本new1的部分向量:\\n',new1_vector[:20])\n",
    "    new2_vector=get_vector(new2_keywords,model)\n",
    "    print('文本new2的部分向量:\\n',new2_vector[:20])\n",
    "print(\"文本new1和new2的相似度:\",similarity(new1_vector,new2_vector))\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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